Autonomous vehicles may be used on land, in the air, underwater, or in outer space to conduct various reconnaissance and/or surveillance operations while, in some instances, being automatically navigated. Reconnaissance, surveillance, and navigation applications often rely on the recognition of certain objects of interest, or what are sometimes referred to as “targets.” Such targets may have specific structural shapes (i.e., shapes that can be modeled as a ensemble of oval or circular contours) when viewed from a captured aerial image. For example, a surface-to-air missile (SAM) site or oil storage facility will typically include multiple individual storage sites configured in a-prior known arrangements. To simplify the detection process, the individual storage sites may be defined as targets of interest. These targets of interest may be modeled as several oval or circular objects when viewed in a captured aerial image.
Relatively rapid, automated recognition of target configurations is desirable when vast data are collected and processed. A relatively fast response time and relatively high-accuracy increases the likelihood of a successful mission. One of the difficulties in implementing relatively fast and accurate automated recognition has to do with the size variety of potential targets. This size variety is related to variations in the size of actual physical structures, variations in the distance (or range) from the image capturing device to the targets, variations in the resolution and zoom capabilities of various image capturing devices, target obscuration challenges, target perspective orientation, the illumination of the operational conditions, and sensor noise, just to name a few. As a result, accurately detecting and recognizing potential targets often requires searching through a huge spatial and feature space. This can be computationally intensive, time intensive, and concomitantly can also be non-cost-effective.
Hence, there is a need for a system and method of automatically and accurately detecting and recognizing potential targets that is relatively less computationally intensive, relatively less time intensive, and relatively more cost-effective than presently known systems and methods. The present invention addresses at least this need.